Search Results (810)

To effectively address the challenges of complex spatiotemporal conflicts, dynamic obstacle avoidance, and coordinated multi-objective optimization in preemptive multi-UAV logistics delivery within complex airspace, this study proposes a Hybrid Improved Multi-Objective Cuckoo Search algorithm (HI-MOCS) for preemptive multi-UAV cooperative logistics scheduling and planning. To overcome the limitations of conventional MOCS, including a low proportion of feasible solutions under complex constraints, susceptibility to local optima, and uneven distribution of multi-objective solution sets, a multi-constraint physical model and a multidimensional evaluation framework are established for preemptive scheduling. A positive knowledge-transfer mechanism based on the co-evolution of primary and auxiliary populations is developed, in which constraint-violation information is used to guide infeasible solutions toward the feasible region. A hybrid heuristic population initialization strategy combining emergency-order priority and spatial scanning rules is introduced to increase the proportion of high-quality feasible solutions in the initial population. In addition, a nonlinear dynamic adaptive parameter-adjustment strategy is designed to balance global exploration and local exploitation, while an iterative truncation-based environmental selection mechanism using the shortest-distance criterion is employed to improve the distribution quality of the Pareto solution set. The experimental results show that, in the benchmark scenario, HI-MOCS achieves an average increase of 33.26% in the total order completion rate and an average reduction of 15.34% in emergency response time compared with 11 multi-objective optimization algorithms, while also exhibiting favorable performance in terms of flight distance per completed order. The fleet-size analysis shows that the 15-UAV configuration achieves the lowest best mean fitness. The safety-distance analysis indicates that, compared with the other safety-distance settings, the 30 m setting increases the total order completion rate by an average of 26.55%, while reducing emergency response time and flight distance per completed order by 27.36% and 33.72%, respectively. The task-scale analysis shows that the 50-order scenario achieves the lowest best mean fitness. Further ablation experiments demonstrate that, compared with the average performance of MOCS and the four single-strategy variants, the complete HI-MOCS improves the total order completion rate by 20.27%, while reducing emergency response time and flight distance per completed order by 20.71% and 36.18%, respectively. The HV, IGD, and Pareto-front results further confirm that the synergistic effects of the four improvement mechanisms effectively enhance the multi-objective optimization performance and the quality of the nondominated solution set. The current study is still validated under simulation conditions assuming reliable GNSS positioning and communication links, without explicitly considering communication delays. Full article

Traditional particle swarm optimization (PSO) easily falls into premature convergence, while differential evolution (DE) is highly sensitive to fixed control parameters. Existing PSO-DE hybrid frameworks suffer from static population sizes and insufficient cross-population information exchange. This paper proposes PSO-DE-ADP, a hybrid optimizer with sinusoidal adaptive parameters, elite-guided mutation, ring neighborhood-weighted PSO and fitness-driven dynamic dual-population allocation. Four complementary mechanisms are integrated: (i) sine-wave perturbation superimposed on linear decay adaptively adjusts PSO inertia weight, acceleration factors and DE scaling/crossover coefficients to balance search stages; (ii) global elite individuals are embedded into DE mutation to reduce blind random search; (iii) ring topology with weighted learning realizes bidirectional information interaction between PSO and DE subpopulations; (iv) the proportion of PSO/DE individuals is dynamically adjusted according to elite ratio to allocate computing resources. Experiments adopt the CEC2017 30-dimensional benchmark with 30 test functions covering unimodal, multimodal, hybrid and composite landscapes. Compared with 8 state-of-the-art metaheuristics, PSO-DE-ADP achieves the lowest Friedman rank (1.08 vs. 2.23–4.90 for PSO variants; 1.53 vs. 2.07–5.00 for non-PSO algorithms). Ablation tests prove each component significantly boosts accuracy; The algorithm only costs 0.172 s average runtime, superior to all competitors. Statistical Wilcoxon and Friedman tests verify its significant superiority. Future work extends this method to multi-objective, constrained and real engineering optimization tasks. Full article

Autogenous shrinkage is a key factor contributing to early-stage cracking in high-performance concrete (HPC), which significantly affects structural durability and service life. As core components of HPC, supplementary cementitious materials (SCMs) can significantly improve concrete workability, mechanical properties, and durability, as well as reduce the risk of shrinkage cracking in HPC, by regulating hydration kinetics, pore structure, and microstructural evolution. The primary objective of this review is to elucidate the differential mechanisms by which different active pozzolanic materials regulate the autogenous shrinkage of HPC. This paper elucidates the patterns and mechanisms by which typical SCMs in HPC (such as fly ash, slag, silica fume, limestone powder, and nano-silica) affect the autogenous shrinkage of HPC. It analyzes the influence of key factors—including the type of SCMs, dosage, particle characteristics, water-to-binder (w/b) ratio, and composite blending on the autogenous shrinkage of HPC. Research indicates that highly reactive SCMs (such as silica fume and nano-silica) accelerate the self-drying process and increase autogenous shrinkage, whereas low-reactivity SCMs (such as fly ash) suppress autogenous shrinkage through dilution effects and by prolonging the hydration cycle. The combined use of multiple SCMs can achieve synergistic control of autogenous shrinkage and mechanical properties. Furthermore, this paper reviews existing autogenous shrinkage prediction models that account for the influence of SCMs and outlines future research directions. At the same time, this review identifies the limitations that currently exist in the research: there is a lack of a unified quantitative theoretical framework for the synergistic effects of multicomponent admixtures. The applicability of prediction models under multi-field coupling of temperature, humidity, and corrosive media is limited. And there is insufficient experimental data on the long-term shrinkage behavior of new low-carbon admixtures such as rice husk ash and calcined clay, which requires further dedicated research. Full article

Emerging technology topic identification is an important research task in the field of scientific and technological intelligence. To achieve a more comprehensive identification of emerging technology topics, this study proposes a multi-source data fusion framework that integrates three types of data sources: academic publications, patent data, and data from the GitHub open-source platform. In addition, an evaluation indicator system is constructed from four dimensions: growth, novelty, continuity, and impact. During the identification process, the BERTopic topic modeling approach is employed to uncover latent topics within the data, while the entropy weight method is applied for objective weighting, ultimately enabling the identification of emerging technology topics. The results indicate that the identified emerging technology topics include, but are not limited to, large language model-driven intelligent interaction, embodied intelligence perception, context memory management, and multimodal generation. Among the data sources, GitHub data provide earlier signals of technological evolution. Incorporating open-source platform data into the framework can effectively alleviate the lagging issues associated with traditional data sources. The proposed framework provides a more comprehensive research perspective for emerging technology topic identification. Full article

Rotating machinery prognostics is essential for ensuring the reliability and operational safety of industrial systems. Although gated recurrent units (GRUs) have achieved competitive performance in remaining useful life (RUL) prediction, whether their internal dynamics are consistent with irreversible degradation mechanisms remains largely unexplored. This study revisits GRU-based prognostics from a physics-consistency perspective and analyzes the potential mismatch between gating mechanisms and degradation evolution. A full-life benchmarking framework is developed based on the XJTU-SY bearing run-to-failure dataset. A training-based health indicator (HI) is constructed through multi-domain vibration feature extraction and principal component analysis, where the degradation-state representation and RUL prediction objective are explicitly distinguished to avoid physically inconsistent supervision. Several representative approaches, including statistical models and deep learning architectures (LSTM, GRU, TCN, and Transformer), are evaluated using both prediction accuracy metrics (RMSE, MAE, and R²) and physical consistency criteria (monotonicity index, monotonicity violation index, and degradation trend consistency). Experimental results demonstrate that superior prediction accuracy does not necessarily guarantee physically consistent degradation modeling. Although GRU provides competitive RUL prediction performance, its hidden-state evolution and gating responses exhibit noticeable non-monotonic behaviors during degradation progression. These findings reveal a potential discrepancy between prediction-oriented recurrent learning mechanisms and irreversible degradation dynamics, highlighting the importance of incorporating physics-consistency evaluation into reliable data-driven prognostic models. Full article

Coal-based solid wastes, including coal gangue and fly ash, can be extensively utilised in cemented backfill materials. However, the slump, bleeding rate, and mechanical strength of these materials depend nonlinearly on the mixture composition, particle size, solids concentration, and curing conditions, complicating the multi-performance mixture design. This study developed an ensemble-learning framework for the target-specific performance prediction and empirical-uncertainty-aware inverse design of coal-based solid-waste cemented backfill materials. A literature-derived database containing 720 observations and 11 predictors was established. After the target-specific filtering of missing responses, 214 observations were available for the slump, 284 for the bleeding rate, and 711 for the uniaxial compressive strength (UCS). Support vector regression (SVR), Bagging-SVR, AdaBoost-SVR, and Stacking-SVR were evaluated using 20 repeated random 80:20 holdout partitions to assess the within-database predictive performance. Bagging-SVR achieved the lowest mean inner-cross-validation RMSE for all three responses. Its mean test R² values were 0.969, 0.871, and 0.965 for the slump, bleeding rate, and UCS, respectively, with corresponding RMSE values of 2.228 cm, 1.206 percentage points, and 1.575 MPa. SHAP analysis showed that the coal-gangue particle size and solids concentration received the largest model attributions for the slump and bleeding-rate predictions, whereas the cement content and curing time received the largest attributions for the UCS prediction. The selected Bagging-SVR models were subsequently coupled with multi-objective differential evolution incorporating empirical prediction bounds, component mass balance, and target-specific five-nearest-neighbour applicability-domain constraints. The selected compromise candidate had a solids concentration of 79.46% and coal-gangue, fly-ash, and cement dry-solid mass fractions of 63.29%, 24.95%, and 11.76%, respectively. Its predicted slump, bleeding rate, and 28 d UCS were 21.19 cm, 1.85%, and 6.36 MPa, respectively. The nominal empirical upper bound of the bleeding rate was 3.83%, and the lower bound of the UCS was 3.74 MPa, both satisfying their prescribed limits. However, the nominal slump interval of 15.70–26.65 cm was not fully contained within the prescribed range of 18–26 cm. Full article

With the rapid evolution of next-generation mobile communications, semantic communication has emerged as an intelligent communication paradigm capable of surpassing the Shannon limit. A fundamental prerequisite for this paradigm is the synchronization of background knowledge between the transmitter and receiver, making the semantic knowledge base (SKB) a critical cornerstone. However, effectively selecting appropriate content from massive cloud-based knowledge repositories for edge deployment remains a significant challenge. This paper conducts systematic research to address the key issues in the flow deployment of SKBs at the edge, including insufficient adaptation to personalized preferences, inadequate timeliness management, and the complexity of multi-objective optimization. First, a comprehensive system model is constructed, integrating user preferences, knowledge relevance, transceiver matching degree, and the Age of Information (AOI). Second, the Generative Adversarial Network (GAN)-assisted Preference-based Reinforcement Learning (GaPbRL) algorithm is proposed. The experimental results demonstrate that this method outperforms traditional schemes in terms of knowledge-base hit rate, transceiver matching degree, and algorithm convergence speed, while significantly reducing the overhead of manual fine-tuning. This study provides a robust framework for the personalized and efficient cloud–edge collaborative deployment of SKBs. Full article

High-entropy alloys (HEAs) are a unique metallic alloy that was initially recognized for the possibility of stabilizing simple solid-solution phases through high configurational entropy. Research over the past two decades, however, has shown that their behavior is far more complex. Phase formation, microstructural evolution, and surface performance arise from the combined influence of composition, atomic interactions, processing history, and the surrounding environment. This paper reviews the connections between these aspects moving from the bulk alloy to the surface. The thermodynamic and empirical criteria utilized for prediction of phase formation and reasons behind ignoring the factors such as ordering, segregation, metastability, and processing defects are described. Further, the influence of casting, rapid solidification, coating deposition, and thin-film processing on the microstructure that will interact with catalytic or corrosive environment is reviewed. Electrocatalysis and corrosion resistance are considered as two strongly coupled surface phenomena rather than separate fields of application. Quantitative comparison of exemplary high-entropy alloy systems shows the influence of the alloying approach and surface development on the catalytic properties, surface reconstruction, selective dissolution, passive film formation, and localized corrosion. The potential of CALPHAD modeling, density functional theory, machine learning, and multi-objective optimization for a better alloy selection in the field of high-entropy alloys is reviewed as well. We identified that the success of HEA design is not only in choosing the right composition but rather in controlling the phases, defects, interfaces, and surface of the HEA. Full article

High-risk industries (HRI) face persistent safety challenges due to complex environments and multi-factor risks. Traditional manual monitoring is inefficient and reactive. While computer vision (CV) and deep learning (DL) have enabled automated risk perception, existing research lacks systematic review of the transition toward risk cognition. This systematic review, following PRISMA 2020 guidelines, searched Web of Science, Scopus, and IEEE Xplore for studies published from January 2021 to December 2025. After two-stage screening, 108 eligible studies were included. These studies span construction, mining, oil and gas, petrochemical, rail, energy, and maritime industries, with perception-layer applications predominating while cognition and early warning layer studies remain limited. We propose a “perception–cognition–early warning” framework to map the evolution from data to information, knowledge, and actionable decisions. Our findings reveal that visual perception technologies (e.g., Personal Protective Equipment (PPE) detection, object tracking) have matured, but significant bottlenecks persist in multimodal information fusion, knowledge reasoning, and decision-making. Achieving a true cognitive leap requires multimodal semantic alignment, scene graph construction, and causal inference. The proposed framework enables practitioners to design cognitive safety vision systems with scene understanding and interpretable decision-making capabilities. Key implementation strategies include addressing challenges in few-shot learning, cross-domain generalization, and human–machine collaboration. By integrating AI-driven perception, cognitive reasoning, and proactive intervention, this review supports the United Nations Sustainable Development Goals. Relevant goals include Goal 3 (health and well-being), Goal 8 (decent work), and Goal 9 (industry and innovation). Full article

Objectives: We sought to evaluate the early efficacy of faricimab (Vabysmo^®) in treating diabetic macular edema (DME) and to explore the predictive value of multi-slice optical coherence tomography (OCT) biomarkers for anatomical and visual outcomes. Methods: In this retrospective cohort study, 26 anti-VEGF-naive DME patients (36 eyes) treated with intravitreal faricimab were analyzed from baseline through 6 months of follow-up. Best-corrected visual acuity (BCVA) was recorded, while central subfield thickness (CST) and various OCT biomarkers were evaluated using multi-slice OCT quantitative analysis. Logistic and linear regression models were utilized to examine predictive factors, and scatter plots were employed to assess the correlation between anatomical improvement and functional visual gain. Results: Changes in CST and BCVA, along with the evolution and predictive power of OCT biomarkers—including vitreomacular interface (VMI), epiretinal membrane (ERM), disorganization of the retinal inner layers (DRIL), intraretinal cysts (IRCs), hyperreflective foci (HRF), hard exudates (HEs), large outer-nuclear-layer cavities (LONLCs), ellipsoid zone disruption (EZD), and subretinal fluid (SRF)—were assessed. Post-treatment CST demonstrated rapid and significant reduction, decreasing from 377.7 μm (95% CI: 347.0–408.4) at baseline to 312.8 μm (95% CI: 287.5–338.2; p < 0.0001) at month 3 and 291.5 μm (95% CI: 275.6–307.4; p < 0.0001) at month 6, with an approximately 48 μm reduction post-first injection. Overall intraocular pressure (IOP) and BCVA showed no statistically significant improvement. Baseline analysis indicated that EZD was significantly associated with older age (p = 0.029), worse initial BCVA (p = 0.005), and thicker CST (p = 0.002). After adjusting for initial CST in the linear regression model, we identified the presence of baseline HEs as the sole independent predictor of substantial anatomical improvement (B = 45.9, p = 0.002). Conclusions: Faricimab demonstrated rapid and significant early anatomical improvements. Baseline HEs independently predicted the extent of CST reduction, potentially reflecting the greater fluid burden of eyes with more severe barrier breakdown. Baseline EZD was observed exclusively among eyes that did not achieve complete anatomical remission, although the small number of EZD-positive eyes (n = 7) precludes firm conclusions. Full article

Multifunctional land use has gained increasing attention for reconciling societal (life function), economic (production function), and environmental (ecological function) development needs and addressing sustainable land use challenges in rapidly urbanizing regions. Consistent with mainstream international land use functions (LUFs), this study’s production-living-ecological (PLE) framework covers three key land functions, matching global research paradigms. To develop a sustainable county-level land use quantitative structure optimization model, this study innovatively integrates a mechanical equilibrium model with the multi-objective genetic algorithm (NSGA-II), overcoming the limitations of conventional qualitative production-living-ecological spaces (PLES) optimization. Furthermore, by establishing a mapping relationship between urbanization drivers and PLES functional evolution, it also enables structural optimization across urbanization subsystems. The optimization results indicate the following adjustment directions: southern coastal and southwestern counties require targeted population and socioeconomic urbanization improvements, while northern counties demand differentiated ecological urbanization regulation, and southeastern coastal areas should prioritize ecological protection. Most counties exhibit cropland and construction land expansion alongside woodland shrinkage, featuring expanded production and living spaces but contracted ecological functions. In contrast, certain counties achieve coordinated sustainability by eliminating inefficient construction land. This study operationalizes macroscopic PLE coordination into feasible quantitative strategies, enriching optimization methodologies and providing transferable insights for territorial spatial governance. Full article

Low-emissivity (low-E) coatings are among the most effective thin-film technologies for reducing radiative heat losses and controlling solar heat gain in buildings, which account for approximately 30–40% of global primary energy consumption. This expanded review follows the technological evolution of low-E glazing from early transparent-conductor “heat mirrors” to modern multi-silver dielectric/metal/dielectric (D/M/D) architectures and emerging functional coatings. Four complementary perspectives are addressed: (i) the materials employed, from silver-based multilayers and transparent conducting oxides (ITO, FTO, AZO, GZO) to seed, blocker, and protective dielectric layers; (ii) the deposition systems, contrasting on-line pyrolytic/CVD “hard” coatings with off-line magnetron-sputtered “soft” coatings, together with ALD, sol–gel, and evaporation routes; (iii) the methods of analysis used to correlate microstructure, composition. and interfaces with optical, electrical, and thermal behaviour (XRD, XRR, SEM/TEM, AFM, XPS, SIMS, spectrophotometry, ellipsometry, emissivity, and U-value metrology according to EN 410/EN 673 and ISO 9050); and (iv) the functional performance of low-E stacks in insulating glass units, vacuum glazing, retrofit films, and smart-window systems across climate zones. Persistent research gaps are identified in long-term durability and ageing, indium-free scalable materials, standardized accelerated testing, and multi-objective design of thinner, more selective, and more robust stacks. Full article

To address the difficulty of simultaneously optimizing execution time, energy consumption, and motion smoothness for six-degrees-of-freedom (6-DOF) industrial robotic manipulators in continuous operations such as high-speed handling and assembly, this study proposes a multi-objective joint-space trajectory optimization method based on an improved Dung Beetle Optimizer (IDBO). First, to adapt DBO to constrained multi-objective trajectory optimization, an external archive, nondominated sorting, and a crowding distance mechanism were incorporated to construct and maintain the Pareto solution set. Second, Sobol low-discrepancy sequence initialization was used to improve the initial population distribution. Adaptive Lévy flight perturbation and an adaptive random perturbation mutation strategy for non-elite individuals were further combined to enhance global exploration and reduce the risk of premature convergence. Finally, seventh-degree B-spline curves were adopted to construct a continuous joint-space trajectory model. Based on this model, a multi-objective trajectory optimization model was established by considering total execution time, energy consumption, and jerk as the optimization objectives. Furthermore, simulation experiments were conducted using MATLAB R2024a, and the proposed algorithm was compared with multi-objective particle swarm optimization (MOPSO), an improved multi-objective differential evolution algorithm (GMODE), the nondominated sorting genetic algorithm II (NSGA-II), and the multi-objective Dung Beetle Optimizer (MODBO). The results showed that the proposed algorithm obtained a Pareto front with better convergence, wider coverage, and a more uniform distribution. Compared with MOPSO, GMODE, NSGA-II, and MODBO, the mean hypervolume (HV) obtained by IDBO was 13.24%, 8.43%, 2.29%, and 2.34% higher, respectively; the mean inverted generational distance (IGD) was 9.45%, 26.68%, 16.35%, and 11.05% lower, respectively; and the mean Spacing value was 55.09%, 64.81%, 58.95%, and 14.06% lower, respectively. The execution time, energy consumption index, and joint jerk of the selected compromise solution were 5.27 s, 2.48, and 9.79, respectively, which were 29.73%, 43.51%, and 18.14% lower than those of the unoptimized trajectory. Constraint verification showed that the peak joint velocities, accelerations, and jerks remained within their prescribed limits. These results indicate that the proposed method provides a feasible approach for multi-objective joint-space trajectory planning of industrial robotic manipulators. Full article

To investigate the regulation mechanism of poly (alkyl methacrylate) (PMA) additives on the viscosity–temperature characteristics and tribological performance of different types of base oils, Group III mineral base oils and Group IV PAO synthetic base oils were selected as the research objects. Rheological and boundary-lubrication tests were systematically conducted at different PMA addition levels, with emphasis on comparatively analyzing the polymer conformational evolution, interfacial adsorption behavior, and lubrication-performance response induced by differences in the solvent polarity of the base oils. The results showed that the modification effect of PMA on base oils exhibited pronounced matrix dependence and non-monotonic concentration characteristics, and its lubrication-regulating behavior was dominated by the coupled trade-off among polymer solubility, molecular conformational stability, and interfacial competitive adsorption ability. In the mineral-oil system, where the base oil acts as a good solvent, the solubility parameters of PMA and the base oil are well matched, allowing the polymer molecular chains to sufficiently swell and extend and providing excellent adsorption and film-forming ability. With increasing PMA concentration, the viscous-flow activation energy of the oil continuously decreased, while the viscosity–temperature performance and boundary-lubrication stability were simultaneously improved, resulting in stable and reliable modification effects. In contrast, in the PAO synthetic-oil system, where the base oil acts as a poor solvent, the PMA molecular chains tend to adopt coiled conformations, with their conformations being highly sensitive to temperature and shear rate, while their interfacial adsorption ability is weaker than that of the base-oil molecules. An optimum critical PMA concentration of 1.0 wt% was observed in this system. Above this concentration, intramolecular friction increased, resulting in deterioration of both viscosity–temperature characteristics and friction performance. This study clarifies the differentiated modification mechanisms of PMA in base oils with different polarities and reveals the dominant role of solvent effects in polymer rheological and tribological behaviors, thereby addressing the insufficient understanding in existing studies of the non-monotonic modification behavior of PMA and its multi-factor coupled mechanism. The findings provide a theoretical basis for PMA structural selection and precise concentration formulation in lubricating oils under different operating conditions and have important engineering application value for optimizing viscosity–temperature performance over a wide temperature range, improving service stability under boundary lubrication, and balancing lubrication reliability with formulation economy. Full article

Basalt fiber-reinforced concrete (BFRC), reinforced with chopped basalt fibers having lengths ranging from 12 to 30 mm and diameters ranging from 0.013 to 0.020 mm, is a sustainable construction material with enhanced strength and durability; however, its complex nonlinear behavior makes accurate prediction and optimal mix design challenging. This study proposes an integrated machine learning framework combining evolutionary optimization, multi-objective optimization, and explainable artificial intelligence (XAI) for BFRC strength prediction and mix design optimization. The proposed framework further incorporates a graphical user interface (GUI) deployment to enhance practical usability and support engineering decision-making. Random Forest, Gradient Boosting Regressor, and XGBoost models were optimized using Genetic Algorithms, Particle Swarm Optimization, and Differential Evolution, while NSGA-II was employed to identify optimal trade-offs between compressive strength and splitting tensile strength. SHAP analysis was applied to interpret the influence of key mix parameters on strength prediction. The optimized models achieved high prediction accuracy, with R² values of 0.88 for compressive strength and 0.95 for splitting tensile strength, demonstrating the effectiveness of the proposed framework. The developed GUI provides a practical decision-support tool for sustainable and performance-oriented BFRC mix design. Full article